Skill UI

A structured methodology for coordinating multiple, simultaneous investigation threads to determine the root cause of complex system failures, performance regressions, or integration bugs. This skill decomposes a large problem into independent tracks (e.g., database, application code, infrastructure), assigns ownership, and manages structured sync points (Continue/Pivot/Converge) to converge on a solution faster than serial debugging. Essential for high-stakes production incident response.

Generates a structured, actionable integration testing plan for data access artifacts migrating from Oracle to PostgreSQL. The tool analyzes a single target project to identify repositories, DAOs, and service layers, prioritizing tests based on Oracle-specific features and known behavioral differences (e.g., date/time handling, type coercion). It ensures comprehensive test coverage for crucial database interactions.

This tool acts as an expert prompt engineer, guiding users through a systematic discovery process to create highly structured, production-ready prompts for AI assistants like GitHub Copilot. It covers defining persona, specifying complex tasks, managing context variables, integrating tools, and enforcing strict output standards, ensuring the resulting prompt is comprehensive and actionable for advanced AI agents.

Scaffold a production-ready Python MCP server with uv-based tooling, FastMCP configuration, async tools, structured schemas, and comprehensive error handling plus deployment/testing guidance.

Analyzes Copilot guidance files and repository docs to craft structured README.md content, detailing the project purpose, architecture, technology stack, workflows, coding standards, testing approach, and contributing advice for fellow developers.

A persistent, graph-based knowledge base designed to accumulate structured knowledge throughout the entire research lifecycle. It stores and connects diverse entities—including published papers, novel ideas, conducted experiments, and testable claims. Inspired by LLM Wiki patterns, it ensures knowledge compounding, meaning the wiki becomes progressively smarter with every piece of input, rather than requiring re-derivation for every query.

Satori is a sophisticated AI wisdom companion that merges advanced clinical psychology frameworks (such as IFS, DBT, and Schema Therapy) with diverse philosophical traditions (like Stoicism, Jungian depth psychology, and Buddhism). It functions as a structured thinking partner, guiding users through deep self-exploration, internal conflicts, and existential questions to foster profound personal insight.

This skill manages a persistent, structured personal knowledge base (Wiki). It provides core operations for ingesting external articles, synthesizing insights from conversations, and querying stored knowledge. By maintaining structured files, index lists, and cross-references, it helps build a comprehensive knowledge graph, ensuring all insights are captured and easily retrievable.

This skill is designed to manage complex, multi-step tasks by coordinating the efforts of multiple specialized AI agents. It provides structured methods for listing available agents, initiating real-time conversations, submitting complex background tasks (such as data analysis or report generation), tracking task status, and maintaining conversational context using session IDs. It is essential when a core task requires diverse expert knowledge or sequential collaboration.

This skill aggregates and summarizes the latest news from multiple authoritative Chinese sources across various categories, including Politics, Finance, Society, World, Tech, Sports, and Entertainment. It utilizes web browsing tools to open specified URLs, take content snapshots, and extract key headlines and detailed summaries, providing the user with a concise, structured report.

Audits crawlability, metadata, structured data, Core Web Vitals, and content signals while planning keyword mapping, internal linking, and sitemap fixes so teams can steadily boost organic visibility.

A comprehensive tool for planning and auditing programmatic SEO pages generated at scale from structured data sources (CSV, JSON, APIs). It guides best practices for designing templates, defining scalable URL systems, establishing internal linking strategies (Hub/Spoke), and implementing robust quality gates. Use this when ensuring content uniqueness, preventing thin content penalties, and mitigating risks associated with large-scale, data-driven content generation.